Based partly on experiences in other countries, the paper discusses potential reforms to short-time compensation programmes and unemployment insurance, which could help limit the short- and long-term harm from layoffs going forward.We examine trends in employment, earnings and incomes over the last two decades in the United States, and how the safety net has responded to changing fortunes, including the shutdown of the economy in response to the COVID-19 pandemic. The US safety net is a patchwork of different programmes providing in-kind as well as cash benefits, and it had many holes prior to the pandemic. In addition, few of the programmes are designed explicitly as automatic stabilisers. We show that the safety net response to employment losses in the COVID-19 pandemic largely consists only of increased support from unemployment insurance and food assistance programmes, an inadequate response compared with the magnitude of the downturn. We discuss options to reform social assistance in the United States to provide more robust income floors in times of economic downturns.In this paper, we suggest an approach to analysing policies relating to the COVID-19 pandemic. We discuss the formulation of policy and sketch how the approach can be applied to different specific challenges as policymakers try to make difficult choices for managing the pandemic and protecting the economy and society.While we have a rich understanding of the motivations of disadvantaged group members to act collectively with their group, especially the important role played by identification, we know less about the disadvantaged's motivations to engage in joint action with the advantaged. This research examines the role of identification in predicting joint and ingroup collective action in intergroup conflicts. Since joint action inherently diffuses the perception of "us versus them", we propose that identification predicts ingroup action, but not joint action. We also examine conflict intensity as a moderator, and examine how changing identification is linked to change in support for joint action. We test these hypotheses in a three-wave longitudinal study in the Palestinian-Israeli conflict. Results support our hypotheses, demonstrating that identification positively predicts ingroup action but not necessarily joint action, and that when conflict intensifies, changes in identification are negatively related to joint action with outgroup members.Distributional justice-measured by the proportionality between effort exerted and rewards obtained-and guilt aversion-triggered by not fulfilling others' expectations-are widely acknowledged fundamental sources of pro-social behavior. We design three experiments to study the relevance of these sources of behavior when considered in interaction. In particular, we investigate whether subjects fulfill others' expectations also when this could produce inequitable allocations that conflict with distributional justice considerations. Our results confirm that both justice considerations and guilt aversion are important drivers of pro-social behavior, with the former having an overall stronger impact than the latter. Expectations of others are less relevant in environments more likely to nurture equitable outcomes.Our analysis, which began as a request from the Oklahoma Governor for useable analysis for state decision making, seeks to predict statewide COVID-19 spread through a variety of lenses, including with and without long-term care facilities (LTCFs), accounting for rural/urban differences, and considering the impact of state government regulations of the citizenry on disease spread.
We utilize a deterministic susceptible exposed infectious resistant (SEIR) model designed to fit observed fatalities, hospitalizations, and ICU beds for the state of Oklahoma with a particular focus on the role of the rural/urban nature of the state and the impact that COVID-19 cases in LTCFs played in the outbreak.
The model provides a reasonable fit for the observed data on new cases, deaths, and hospitalizations. Moreover, removing LTCF cases from the analysis sharpens the analysis of the population in general, showing a more gradual increase in cases at the start of the pandemic and a steeper increase when the second surge occurred.
We anticipate that this procedure could be helpful to policymakers in other states or municipalities now and in the future.
We anticipate that this procedure could be helpful to policymakers in other states or municipalities now and in the future.Large in-person gatherings of travelers who do not socially distance are classified as the "highest risk" for COVID-19 spread by the Centers for Disease Control and Prevention (CDC). From August 7-16, 2020, nearly 500,000 motorcycle enthusiasts converged on Sturgis, South Dakota for its annual rally in an environment without mask-wearing requirements or other mitigating policies. This study is the first to explore this event's public health impacts. First, using anonymized cell phone data, we document that foot traffic at restaurants/bars, retail establishments, and entertainment venues rose substantially at event locations. Stay-at-home behavior among local residents fell. Second, using a synthetic control approach, we find that the COVID-19 case rate increased substantially in Meade County and in the state of South Dakota in the month following the Rally. Finally, using a difference-in-differences model to assess nationwide spread, we find that following the Sturgis event, counties outside of South Dakota that contributed the highest inflows of rally attendees experienced a 6.4-12.5% increase in COVID-19 cases relative to counties without inflows. Our findings highlight that local policy decisions assessing the tradeoff between local economic benefits and COVID-19 health costs will not be socially optimal in the presence of large contagion externalities.A computable general equilibrium model linked to a microsimulation model is applied to assess the potential short-term effects on the South African economy of the ongoing COVID-19 pandemic. With a particular focus on distributional outcomes, two simulations are run, a mild and a severe scenario. The findings show significant evidence of decline in economic growth and employment, with the decline harsher for the severe scenario. The microeconomic results show that the pandemic moves the income distribution curve such that more households fall under the poverty line while at the same time, inequality declines. https://www.selleckchem.com/products/palazestrant.html The latter result is driven by the disproportionate decline in incomes of richer households while the poorest of the poor are cushioned by government social grants that are kept intact during the pandemic. The COVID-19 pandemic is still unfolding and its economic modelling as well as the data used to operationalise the model will need to be updated and improved upon as more information about the disease and the economy becomes available.